{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 1),\n",
       " (0, 2),\n",
       " (1, 2),\n",
       " (2, 3),\n",
       " (3, 4),\n",
       " (3, 5),\n",
       " (3, 6),\n",
       " (3, 7),\n",
       " (4, 5),\n",
       " (4, 6),\n",
       " (5, 6),\n",
       " (7, 8)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关节点\n",
    "import igraph as ig\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 使用图公式建图\n",
    "g = ig.Graph.Formula(\n",
    "    \"0-1-2-0, 3:4:5:6 - 3:4:5:6, 2-3-7-8\",\n",
    ")\n",
    "g.get_edgelist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertex ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name\n",
       "vertex ID     \n",
       "0            0\n",
       "1            1\n",
       "2            2\n",
       "3            3\n",
       "4            4\n",
       "5            5\n",
       "6            6\n",
       "7            7\n",
       "8            8"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.get_vertex_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 找到作为顶点序列的关节点\n",
    "articulation_points = g.vs[g.articulation_points()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ig.plot(\n",
    "    g,\n",
    "    target=ax,\n",
    "    vertex_size=0.3,\n",
    "    vertex_color=\"lightblue\",\n",
    "    vertex_label=range(g.vcount()),\n",
    "    vertex_frame_color = [\"red\" if v in articulation_points else \"black\" for v in g.vs],\n",
    "    vertex_frame_width = [3 if v in articulation_points else 1 for v in g.vs],\n",
    "    edge_width=0.8,\n",
    "    edge_color='gray'\n",
    ")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 ('py310')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "49ec5068d4d6d3736c39d3918a16f1ca551a23384109b69898c01c2015d1370c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
